1. Field of the Invention
This invention generally relates to methods and systems for creating a detect sample for a wafer that is suitable for production monitoring and can be re-normalized to a total defect population detected on the wafer.
2. Description of the Related Art
The following description and examples are not admitted to be prior art by virtue of their inclusion in this section.
Fabricating semiconductor devices such as logic and memory devices typically includes processing a substrate such as a semiconductor wafer using a large number of semiconductor fabrication processes to form various features and multiple levels of the semiconductor devices. For example, lithography is a semiconductor fabrication process that involves transferring a pattern from a reticle to a resist arranged on a semiconductor wafer. Additional examples of semiconductor fabrication processes include, but are not limited to, chemical-mechanical polishing, etch, deposition, and ion implantation. Multiple semiconductor devices may be fabricated in an arrangement on a single semiconductor wafer and then separated into individual semiconductor devices.
Inspection processes are used at various steps during a semiconductor manufacturing process to detect defects on wafers. Inspection processes have always been an important part of fabricating semiconductor devices such as integrated circuits. However, as the dimensions of semiconductor devices decrease, inspection processes become even more important to the successful manufacture of acceptable semiconductor devices. For instance, as the dimensions of semiconductor devices decrease, detection of defects of decreasing size has become necessary since even relatively small defects may cause unwanted aberrations in the semiconductor devices.
During production runs, in order to monitor the defectivity of production wafers, typically a sample of defects is created from a total defect population detected on a wafer. That sample of defects is then manually classified (i.e. classification performed by a human expert) one of several possible techniques such as reviewing the sampled defects on a defect review tool such as a scanning electron microscope (SEM). The classification results can then be extrapolated back to defects that are similar to the sampled defects in some manner.
There are three main approaches to production sampling. The first is random sampling in which defects are randomly selected from the detected population. This sampling is a re-normalizable sampling scheme that is very easy to setup, but it is not very efficient in capturing lower count defect types. In some instances, random sampling may rely on spatial diversification by requiring that dies and dusters are not sampled excessively. This spatial diversification introduces subtle bias into the renormalization process, which results in systematic errors that are different for different wafers and that are difficult to correct.
The second is class code based sampling in which defects are selected randomly from bins. This method requires a defect binner (classifier) to be trained and executed on the inspection results. Compared to random sampling, this method is more stable and efficient for well performing binners. The increased efficiency and stability are achieved through the partial diversification provided by the binners. This sampling is re-normalizable. Just as in the case of random sampling, spatial diversification is also typically enforced.
The third is rule-based sampling in which defects are selected based on a set of rules. This scheme is a hybrid scheme that has very similar characteristics to class code based sampling. This sample is also re-normalizable as long as the defects are sampled randomly from the set of defects that satisfy each rule.
There are two main disadvantages of the existing sampling methods. For example, the resulting samples tend to have significant fluctuations from run-to-run even for relatively stable processes. In addition, the sampling tends to be less effective in selecting relatively low count defect types, thus lacking critical information relatively frequently. The existing sampling methods also have a number of secondary disadvantages. For instance, the existing methods require tuning classifiers for class code based sampling. In addition, the classifiers themselves require periodic performance monitoring.
Accordingly, it would be advantageous to develop systems and/or methods for generating a defect sample for a wafer that do not have one or more of the disadvantages described above.